Detecting factors controlling spatial patterns in urban land surface temperatures: A case study of Beijing

Urban heat islands (UHIs) are a worldwide phenomenon that have many ecological and social consequences. It has become increasingly important to examine the relationships between land surface temperatures (LSTs) and all related factors. This study analyses Landsat data, spatial metrics, and a geographically weighted regression (GWR) model for a case study of Hangzhou, China, to explore the correlation between LST and urban spatial patterns. The LST data were retrieved from Landsat images. Spatial metrics were used to quantify the urban spatial patterns. The effects of the urban spatial patterns on LSTs were further investigated using Pearson correlation analysis and a GWR model, both at three spatial scales. The results show that the LST patterns have changed significantly, which can be explained by the concurrent changes in urban spatial patterns. The correlation coefficients between the spatial metrics and LSTs decrease as the spatial scale increases. The GWR model performs better than an ordinary least squares analysis in exploring the relationship of LSTs and urban spatial patterns, which is indicated by the higher adjusted R2 values, lower corrected Akaike information criterion and reduced spatial autocorrelations. The GWR model results indicate that the effects of urban spatial patterns on LSTs are spatiotemporally variable. Moreover, their effects vary spatially with the use of different spatial scales. The findings of this study can aid in sustainable urban planning and the mitigation the UHI effect.

Download Full-text

A physical model-based method for retrieving urban land surface temperatures under cloudy conditions

Remote Sensing of Environment ◽

10.1016/j.rse.2019.05.010 ◽

2019 ◽

Vol 230 ◽

pp. 111191 ◽

Cited By ~ 13

Author(s):

Peng Fu ◽

Yanhua Xie ◽

Qihao Weng ◽

Soe Myint ◽

Katherine Meacham-Hensold ◽

...

Keyword(s):

Physical Model ◽

Land Surface ◽

Urban Land ◽

Model Based ◽

Surface Temperatures ◽

Land Surface Temperatures

Download Full-text

Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations

Remote Sensing ◽

10.3390/rs11141722 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1722 ◽

Cited By ~ 7

Author(s):

Joseph Naughton ◽

Walter McDonald

Keyword(s):

Land Use ◽

Surface Temperature ◽

Environmental Health ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Land ◽

Urban Environments ◽

Surface Temperatures ◽

Land Use Types ◽

Land Surface Temperatures

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health.

Download Full-text